8 February 2010 Image analysis and classification by spectrum enhancement: new developments
Author Affiliations +
Abstract
The "enhanced spectrum" of an image g[.] is a function h[.] of wave-number u obtained by a sequence of operations on the power spectral density of g[.]. The main properties and the available theorems on the correspondence between spectrum enhancement and spatial differentiation, of either integer or fractional order, are stated. In order to apply the enhanced spectrum to image classification, one has to go, by interpolation, from h[.] to a polynomial q[.]. The graph of q[.] provides the set of morphological descriptors of the original image, suitable for submission to a multivariate statistical classifier. Since q[.] depends on an n-tuple, Ψ, of parameters which control image pre-processing, spectrum enhancement and interpolation, then one can train the classifier by tuning Ψ. In fact, classifier training is more articulated and relies on a "design", whereby different training sets are processed. The best performing n-tuple, Ψ*, is selected by maximizing a "design-wide" figure of merit. Next one can apply the trained classifier to recognize new images. A recent application to materials science is summarized.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Giovanni F. Crosta, "Image analysis and classification by spectrum enhancement: new developments", Proc. SPIE 7532, Image Processing: Algorithms and Systems VIII, 75320L (8 February 2010); doi: 10.1117/12.838694; https://doi.org/10.1117/12.838694
PROCEEDINGS
12 PAGES


SHARE
Back to Top